Ozturk, H. | Search bioRxiv

bioRxiv2024

ALS driven by mutant NEK1 aggregation is accelerated by Pml loss, but clinically reversed through pharmacologic induction of Pml-mediated degradation

Germinal mono-allelic loss-of-function mutations of NEK1 drive Amyotrophic Lateral Sclerosis (ALS) at variable penetrance, presumably through haploinsufficiency. Modeling the ALS-associated Arg812Ter mutation in mice revealed that the resulting truncated Nek1 (Nek1t) is aggregation-prone, particularly in alpha-motoneurons (MNs), and drives canonical ALS symptoms when bi-allelically expressed (Nek1t/t). Promyelocytic leukemia (Pml) ablation allows for ALS symptoms to occur even in heterozygote Nek1wt/t animals, mimicking the human situation. Pml precludes disease occurrence by promoting SUMO-facilitated degradation of Nek1t proteins through PML nuclear bodies (NBs). Conversely, Pml induction, achieved by activating the interferon pathway via poly(I:C) treatment, clears Nek1t aggregates in MNs, dramatically reducing ALS-associated symptoms and extending survival by 5 months. Our studies highlight the role of NEK1 aggregates in ALS pathogenesis and identifies activation of interferon pathways as a candidate therapeutic strategy that not only promotes Pml-triggered SUMOylation/degradation of toxic misfolded proteins in vivo, but also facilitates the clearance of protein aggregates, yielding dramatic clinical improvement. These observations validate PML as a relevant therapeutic target in neurodegenerative conditions associated with protein aggregation.

pathology↗

bioRxiv2023

Integration of variant annotations using deep set networks boosts rare variant association genetics

Rare genetic variants can strongly predispose to disease, yet accounting for rare variants in genetic analyses is statistically challenging. While rich variant annotations hold the promise to enable well-powered rare variant association tests, methods integrating variant annotations in a data-driven manner are lacking. Here, we propose DeepRVAT, a model based on set neural networks that learns burden scores from rare variants, annotations, and phenotypes. In contrast to existing methods, DeepRVAT yields a single, trait-agnostic, nonlinear gene impairment score, enabling both risk prediction and gene discovery in a unified framework. On 34 quantitative and 26 binary traits, using whole-exome-sequencing data from UK Biobank, we find that DeepRVAT offers substantial increases in gene discoveries and improved replication rates in held-out data. Moreover, we demonstrate that the integrative DeepRVAT gene impairment score greatly improves detection of individuals at high genetic risk. Finally, we show that pre-trained DeepRVAT scores generalize across traits, opening up the possibility to conduct highly computationally efficient rare variant tests.

bioinformatics↗